Status threat, not economic hardship, explains the 2016 presidential vote

The left behind thesis fits most squarely within the rational choice or “pocketbook” theoretical camp, arguing that voters reward the party that has benefitted them financially and punish the party that did not. Thus, easily accessible information about one’s personal financial wellbeing was the assumed basis for a referendum on the party in office. Consistent with this theme, the “rust belt” of the Northeast and the Midwest played an unexpected role in Trump’s victory, contributing to the widespread claim that economic hardship was largely responsible for Trump’s success (2). Interestingly, this explanation is being popularly embraced by liberals as well as conservatives. As summarized in a recent article in The Atlantic, “Perhaps the clearest takeaway from the November election for many liberals is that Hillary Clinton lost because she ignored the working class” (7). Polling data from throughout the campaign made it clear that less educated whites were indeed Trump’s staunchest supporters, but precisely why this demographic favored him remains unclear.

There are two reasons for skepticism regarding the assumption that personal economic hardship drove Trump support. First and foremost, over many decades of scholarship, evidence of voters politicizing personal economic hardship has been exceedingly rare (8). Although aggregate-level evidence has been suggestive of a public that blames incumbents for general economic downturns and rewards incumbents for economic gains, these relationships seldom hold up at the level of individual economic hardship. For example, those who recently lost jobs are unlikely to blame government policy for their personal circumstances (9), and those who have personally suffered financially under a given administration are no more likely to vote against the incumbent (10, 11). Across a wide range of issues, scholars have found that citizens seldom form policy or candidate preferences on the basis of their family’s personal economic self-interest. This is not to suggest that citizens never do so, but the conditions under which this occurs are very rare (12, 13). Even membership in groups with economic interests that have been helped or hurt seldom changes political preferences (14).

A second reason for skepticism regarding the left behind thesis involves timing. Trump’s victory took place in the context of an economic recovery. Throughout the year preceding the election, unemployment was falling, and economic indicators were on the upswing. Likewise, the dramatic drop in US manufacturing jobs took place during the first decade of the 21st century; since 2010, manufacturing employment in the United States has actually increased somewhat (15). Research on economic voting suggests that recent economic events are most influential for voting (16, 17). Given all of the positive economic indicators, why would 2016 be ripe for an economic backlash? The most common explanation is that it is precisely those who did not recover from the Great Recession of 2008 who elected Trump, those who were left behind by virtue of ongoing joblessness and/or stagnant wages.

One way to understand the surprising public acceptance of openly disrespectful statements about women, minorities, and foreigners is as manifestations of preexisting racist and sexist views; in other words, the 2016 election raised the salience of people’s preexisting views on these topics, so that they mattered more to presidential vote choice in 2016 (5). However, as with the economic hardship thesis, the timing of Trump’s rise to power is curious. How is it that the same American public that elected an African American to two terms as US President subsequently elected a president known to have publicly made what many consider to be racist and sexist statements?

A possible explanation is dominant group status threat. When members of a dominant group feel threatened, several well-established reactions help these groups regain a sense of dominance and wellbeing. First, perceived threat makes status quo, hierarchical social and political arrangements more attractive (18). Thus, conservatism surges along with a nostalgia for the stable hierarchies of the past. Perceived threat also triggers defense of the dominant ingroup, a greater emphasis on the importance of conformity to group norms, and increased outgroup negativity (19, 20). It is psychologically valuable to see one’s self as part of a dominant group; therefore, when group members feel threatened, this prompts defensive reactions. It is precisely this form of group threat that may have motivated Trump supporters (21).

Two forms of group status threat are especially prominent in the United States today. For the first time since Europeans arrived in this country, white Americans are being told that they will soon be a minority race (22). The declining white share of the national population is unlikely to change white Americans’ status as the most economically well-off racial group, but symbolically, it threatens some whites’ sense of dominance over social and political priorities. Furthermore, when confronted with evidence of racial progress, whites feel threatened and experience lower levels of self-worth relative to a control group. They also perceive greater antiwhite bias as a means of regaining those lost feelings of self-worth (23).

Second, Americans feel threatened by the increasing interdependence of the United States on other countries. As recent headlines have warned, “The era of American global dominance is over” (24, 25). Whether such headlines are true remains debatable (26), but the perception of a threat to US global dominance is very real. For example, in 2011, 38% of Americans endorsed the view that “[t]he US stands above all other countries in the world” (27); by 2014, that same percentage was down to 28% (27). This drop has been most precipitous among Republicans. The “China threat” in particular looms large in many American minds (28).

Although economists see globalization as mutually beneficial to countries that participate, Americans increasingly feel that they are not getting their fair share. For example, roughly one-half of Americans view trade as something that benefits job availability in other countries at the expense of jobs for Americans (29). To the extent that the public views the global economy in zero-sum terms, the rise of countries, such as China and India, represents a threat to America’s dominant status. Interestingly, whites’ perceptions of antiblack and antiwhite bias also are also zero sum: that is, the less antiblack bias that whites perceive in a given decade, the greater the antiwhite bias that is perceived (30).

Racial status threat and global status threat are technically separable, but they are difficult to distinguish in practice. Because white male Christians are seen as most prototypically “American” (31), they have the most to lose psychologically if they perceive America and/or whites to be no longer dominant. Given that the 2016 election featured discussions of perceived threats from religious minorities, racial minorities, and foreigners, this generalized sense of threat is likely to have spilled over into multiple arenas. For white Americans, the political consequences of racial and global status threat seem to point in similar directions with respect to issue positions: opposition to immigration, rejection of international trade relationships, and perceptions of China as a threat to American wellbeing.

For two of these three issues—trade and China—trends in public opinion clearly support the thesis of increased threat between 2012 and 2016 (32, 33). For immigration, however, multiple sources instead suggest increasingly supportive attitudes among Republicans and Democrats alike (34). Likewise, to the extent that immigration is perceived as threatening by Americans, scholars find that it is due to the increased economic burden Americans believe immigrants place on the social welfare system rather than a threat to white status (35). Nonetheless, it remains possible that the heightened salience of immigration contributed to Trump’s victory without increasing actual opposition to immigration, consistent with previous findings attributing preference changes to the increased salience of immigration (3).

How plausible is status threat—whether from a sense of declining racial or global status—as an explanation for changes in voting behavior in 2016? With respect to global status threat, the received wisdom from decades of research has long been that “voting ends at water’s edge.” In other words, outside of foreign wars, international affairs are assumed to have little if any electoral importance (36). However, economic globalization has gained prominence in recent years (37). Racial status threat makes perfect sense occurring immediately after 8 y of leadership by America’s first African American president. It is not racism of the kind suggesting that whites view minorities as morally or intellectually inferior, but rather, one that regards minorities as sufficiently powerful to be a threat to the status quo. When members of a dominant group experience a sense of threat to their group’s position, whether it is the status of Americans in the world at large or the status of whites in a multiethnic America, change in people’s sense of their group’s relative position produces insecurity.

Despite multiculturalism’s ostensible goal of inclusion, experimental studies suggest that it is experienced by whites as a form of status threat that produces more negative attitudes toward outgroups of all kinds (38). Simply reminding whites about their impending loss of majority status produces feelings of threat in experimental studies (39), particularly among those who think of the “American way of life” as being white (40). Consequences of exposure to information about impending majority–minority status have included increased conservatism and greater identification with the Republican Party (41) and the Tea Party (42), increased opposition to diversity (41), greater explicit and implicit racial bias, and a stronger preference for interacting with one’s own race (43). In one study, reminding participants about the upcoming racial shift also produced increased support for Trump among both Democrats and Republicans in a white convenience sample (44).

The assumption that respondents’ views were essentially stable over time is incorrect (Table S1). Consistent with the hypothesis that threat leads to greater conservatism, the average party identification for Americans shifted in a slightly but significantly more Republican direction from 2012 to 2016. Also consistent with the threat thesis, the American public became significantly more negative in its views of international trade from 2012 to 2016, and this declining support was especially severe among Republicans (Table S1). SDO rose significantly from 2012 to 2016, indicating an increased perception of threat to dominant groups.

However, in contrast to an increased sense of threat from immigration but consistent with other surveys, the average American became more supportive of a path to citizenship between 2012 and 2016 (34). This change occurred among people identifying with both parties, but it was especially pronounced among Democrats (Table S1). Levels of perceived threat from China did not change significantly over this 4-y period. China was already viewed as threatening in 2012, and levels of threat did not increase in the population as a whole or among members of either party.

Beyond opinion change among the mass public, Fig. 1 illustrates the particularly large shifts that occurred over time in how the Republican and Democratic presidential candidates were perceived on these issues (Table S1). For example, in 2012, the two parties’ candidate positions on trade as well as the average American’s position were all indistinguishable, directly in the middle of the seven-point scale. However, by 2016, the distance between the Democratic placement and that of the average American became more than twice the distance between the Republican candidate and the average American. In other words, the Democratic position on trade became far less tenable for the average American than the Republican position. For perceptions of China as a threat, the Republican candidates’ positions remained the same distance from the average American, but the Democratic position became increasingly distant from the average American. Immigration attitudes became more polarized, with Republican candidate position perceived as more antiimmigration and the Democratic position perceived as slightly more positive.

Table 1 presents the central fixed effects findings, including two independent analyses: one predicting change in Republican thermometer advantage, and a second analysis predicting vote choice among the two major candidates. In each case, a single regression equation is used to evaluate both the impact of changes in issue salience and candidate issue positions. The extent to which within-person change over time in a given independent variable predicts an individual’s likelihood of change in the dependent variable is shown in columns 2 and 6 in Table 1. Columns 4 and 8 in Table 1 tap the extent of issue salience effects (that is, the extent to which preexisting views on a given issue were weighted to a greater or lesser extent in 2016 relative to 2012). Both dependent variables are coded in the direction of higher relative thermometer ratings for the Republican presidential candidate or greater probability of Republican vote in the fixed effects logit regression in Table 1.

Not surprisingly, the shift toward greater identification with the Republican Party from 2012 to 2016 predicted greater support for the Republican candidate in both analyses. Although many suspected that party identification mattered less in 2016 than in 2012, the analyses of issue salience show that this was not the case. The most obvious finding in Table 1 is that, contrary to conventional wisdom, there is little to no evidence that those whose incomes declined or whose incomes increased to a lesser extent than others’ incomes were more likely to support Trump. Even change in subjective assessment of one’s own personal financial situation had no discernible impact on evaluations of Trump or on change in vote choice. Likewise, those who lost a job between 2012 and 2016 were no more likely to support Trump. Table 1 further suggests that change over time in the extent to which a person perceived that trade had influenced his or her family financial situation made no difference to candidate attitudes or preferences.

Across all relevant predictors, Table 1 also provides no evidence that the increased salience of personal economic considerations played a role in increasing Trump’s support relative to Romney. Furthermore, respondents’ immediate geographic context, including unemployment and manufacturing concentration, made no difference, with the sole exception that living in an area with a high median income positively predicted Republican vote choice to a greater extent in 2016; this is precisely the opposite of what one would expect based on the left behind thesis. To examine the possibility that the impact of change in personal financial indicators was masked by including other sources of change in these analyses, I reestimated these models and included only the economic predictors (Table S3). Results reaffirmed the findings in Table 1.

Changes over time in indicators tied to racial/global status threat were far more influential as predictors of change toward greater Republican thermometer advantage and as predictors of greater likelihood of Republican vote choice. As shown in Table 1, increases in SDO significantly predicted changes in Republican vote choice as well as Republican thermometer advantage. When a person’s desire for group dominance increased from 2012 to 2016, so did the probability of defecting to Trump. However, as shown by the insignificant interaction between SDO and wave in both analyses, there is no evidence that those high in preexisting SDO were especially likely to defect to Trump, thus countering the idea that SDO was made more salient in 2016. Instead, it is the increase in SDO, which is indicative of status threat, that corresponded to increasing positivity toward Trump.

Mass opinion changes on status threat-related issues were not, by themselves, the driving force in increasing affinity for the Republican candidate. Instead, increasing relative distance from the Democratic candidate on threat-related issues, such as immigration and China, consistently predicted Trump support in a positive direction, whereas decreasing relative distance from the Republican candidate on trade and China also predicted change in the direction of voting for Trump. These consistently significant coefficients indicate that change over time in the candidates’ perceived positions relative to those of individual respondents had a significant impact in increasing support for Trump. The pattern in Table 1 makes it clear that it was change in how the candidates positioned themselves on status threat-related issues combined with smaller changes in public issue opinions that predicted increasing support for the Republican candidate in 2016.

The greater the distance from the Democratic candidate on trade, immigration, and China relative to that same distance in 2012, the more likely they were to defect toward Trump; the more the distance from the Republican candidate increased on these same issues, the less likely they were to shift toward Trump. Thus, changing respondent opinions in combination with realignments by the candidates on these same issues combined to make them important to changing candidate preferences. The large significant coefficient associated with perceived change in the national economy is consistent with the idea that those perceiving the economy as improving were less likely to defect toward the Republicans; however, national economic perceptions are well-known to be rationalized from candidate preferences, thus raising serious suspicions about endogeneity (53).

The analyses in the presidential vote choice columns in Table 1 show what changed in predictors of vote choice among those validated to have actually voted in 2016 who reported voting for one of the two major party candidates. These voters are obviously most important to what happened in 2016. In addition to shifting party identification, which fueled shifts toward the Republican candidate, voters who shifted to become Trump voters between the two elections seem to have done so because of increasing distance between their own views and those of the Democratic candidate on trade, immigration, and China as well as due to the decreasing distance between their own views and those of the Republican candidate. These large significant coefficients suggest that these issues were important engines of change. In addition, those whose SDO level increased, indicating a rising sense of threat to the dominant group’s status, were particularly likely to shift in support of Trump.

What is perhaps most interesting about Table 1 is what it reveals about the appropriate theoretical model for understanding shifting vote preferences from one election to the next. The dominant theoretical model for understanding electoral changes has long been that campaigns change vote choice not so much by changing opinions but by making some issues more salient than others, thus advantaging one candidate over another. However, based on Table 1, this does not seem to be the case. A quick glance down the columns representing the issue salience results (that is, the interactions between 2012 values of these predictors and wave) provides no evidence that changes in vote choice from 2012 to 2016 were a function of changing issue salience. The only significant coefficient suggests that areas higher in percentage manufacturing employment were less likely to shift toward Trump.

Estimating effect sizes using fixed effects analysis is more complex, because one must take into account not only the size of the fixed effects coefficients representing the effects of change on change but also, the extent to which the amount and direction of over-time change in a given variable are ones that help to explain increases as opposed to decreases in support for the Republican candidate. Table S1 summarizes the extent and direction of change over time in each of the independent variables. For example, party identification shifted slightly in the Republican direction, consistent with the idea that threat generally leads to more conservative views. Economic changes are consistent with what one would expect during an economic upturn. Thus, even if changes in pocketbook variables had predicted change in Republican candidate support (which they did not), their net impact would have been decreasing Republican candidate support.

Drawing on the analysis of vote choice among validated voters in model 2 in Table 1, I estimated total net change due to each significant independent variable for the model predicting changing vote choice from 2012 to 2016. To assess net effects, I computed the predicted probabilities of change using the fixed effects model and then evaluated the impact of the average extent of change between 2012 and 2016 in each of these independent variables while holding all other variables at their wave 0 means. The difference between the wave 0 and wave 1 probabilities provides the marginal impact of one variable (Table S2). These estimates represent how the predicted probabilities change as one variable of interest goes from its wave 0 to wave 1 mean value. It takes into account both the extent of change in that variable that occurred on average, and the strength of change in this variable in predicting change in Republican vote choice. For ease of interpretation, Fig. 2 combines the two candidate distance estimates for each issue.

Although the extent of change due to any one variable is limited, the candidates’ changing relative positions on trade account for the greatest net impact on Trump support. By 2016, the Democratic candidate had become much farther away from the average American on this issue. This increasing distance combined with a large coefficient indicating its impact on change in vote choice meant that trade views increased the probability of voting for Trump by almost 0.05. China threat also increased the Republican candidate’s advantage to a lesser extent, roughly one-half the magnitude of trade’s impact. Not surprisingly, immigration hurt Republican vote choice more than it helped, mainly because relative to Democratic candidates, Trump moved farther away from voters whose more moderate positions on this issue were closer to those of Romney in 2012 than Trump in 2016. The marginal effect of shifting party identification accounted for another 0.01–0.02 increase in the predicted probability of voting for the Republican candidate. Although these few issues are not intended to account for all changes between 2012 and 2016 that might have brought about a change in vote choice, given the narrow margins of victory in many states, their effects could have substantial consequences.

Rising SDO accounted for another 0.01 increase in the probability of voting for the Republican candidate. It is also possible that it had indirect effects on people’s issue positions, since opposition to trade and perceptions of China as a threat are known to be fueled by levels of and increases in SDO (29, 51, 52). In short, perceptions of Republican candidate issue positions in 2016 were altered in what were more appealing, status threat-defensive directions for the average voter. The shift toward an antitrade stance was a particularly effective strategy for capitalizing on a public experiencing status threat due to race as well as globalization.

To what extent are these results convincing with respect to the lack of effects from personal economic hardship? Could Trump’s popularity be due to anticipated future financial difficulties rather than a referendum on what had already occurred? Cross-sectional analyses allowed me to examine the possibility that economic anxiety about the future was related to Trump support. Three questions explicitly asked respondents about their concern over (i) not having saved enough for retirement, (ii) not being able to pay medical bills, and (iii) not being able to pay for educational expenses. As shown in Table S4, whether using the sample as a whole or whites only, the results are the same. Concern about future expenses does not predict greater support for Trump. In addition, Trump supporters favor a smaller safety net, contradicting expectations that they are concerned about those facing economic hardship. Consistent with panel evidence, there is little support for the left behind thesis from the cross-sectional indicators of past economic hardship or anticipated hardship.

Another limitation in the panel analyses is that I do not provide direct evidence that dominant groups feel threatened. Instead, I infer this from rising SDO and changing issue attitudes that suggest hunkering down in a protective manner. To address this shortcoming, the cross-sectional data illustrate how dominant group membership affected Trump support as well as whether those who reported that dominant groups were threatened were more likely to support Trump. Table S4 further confirms that whites and men were more likely to support Trump. More to the point, feeling that “the American way of life is threatened” is a consistent predictor of Trump support. In addition, respondents were asked to what extent various groups in America were discriminated against, including Christians, Muslims, men, women, whites, blacks, and Hispanics. If threat to dominant group status is an underlying cause of Trump support, the extent to which people perceive dominant social groups, such as men, Christians, and whites, as discriminated against more than lower status groups should predict support for Trump. Table S4 shows that perceived discrimination against high-status groups does indeed have a substantial impact on the likelihood of supporting Trump, even in a fully saturated model. Largely, the same individuals who perceive whites as more discriminated against than minorities also see Christians and men as experiencing greater discrimination than Muslims and women, despite the former groups’ dominant status. The status threat explanation is thus consistent with others’ interpretations emphasizing gender, race, and religion (3, 5). Furthermore, indicators that were present in both datasets, such as SDO, opinions on trade, and threat from China, produced similar results, adding confidence to this interpretation.

The cross-sectional survey replicates the strong relationship with education shown throughout the election. More importantly, it provides a better understanding of what precisely education represents. In Table S5, model 1, I replicate the strong relationship between lack of college education and Trump support using only demographics as predictors. In model 2, I examine what happens to education’s predictive power when measures of personal economic wellbeing are also included in the model. Finally, in model 3, I drop the economic variables and instead, include indicators corresponding to status threat toward dominant groups. As summarized in Fig. 3, regardless of which outcome measures I examined, including indicators of economic status did not eliminate the impact of education. It reduced education’s impact somewhat for the feeling thermometer measure, but for Trump/Clinton vote, the impact of education remained constant. However, after the relationship between Trump support and perceived status threat is taken into account, even lack of a college education no longer predicts Trump support for any of the measures. These findings strongly suggest that group-based status threat was the main reason that those without college educations were more supportive of Trump.